Model Compression

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1111–1120 of 1356 papers

Title	Date	Tasks	Status
Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images	Mar 27, 2020	6D Pose Estimation using RGBDomain Adaptation	—Unverified
A Survey on Contextual Embeddings	Mar 16, 2020	Model CompressionSurvey	—Unverified
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework	Mar 13, 2020	Model CompressionPrivacy Preserving	—Unverified
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations	Mar 13, 2020	Graph EmbeddingGraph Representation Learning	—Unverified
Knowledge distillation via adaptive instance normalization	Mar 9, 2020	Knowledge DistillationModel Compression	—Unverified
Pacemaker: Intermediate Teacher Knowledge Distillation For On-The-Fly Convolutional Neural Network	Mar 9, 2020	Knowledge DistillationModel Compression	—Unverified
Adaptive Neural Connections for Sparsity Learning	Mar 5, 2020	Model CompressionNetwork Pruning	—Unverified
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation	Feb 28, 2020	Knowledge DistillationMemorization	—Unverified
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT	Feb 27, 2020	Model Compression	—Unverified
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks	Feb 23, 2020	Model Compression	CodeCode Available

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All datasets ImageNet QNLI

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ADLIK-MO-ResNet50+W4A4	Top-1	77.88	—	Unverified
2	ADLIK-MO-ResNet50+W3A4	Top-1	77.34	—	Unverified
3	ResNet-18 + 4bit-1dim model compression using DKM	Top-1	70.52	—	Unverified
4	MobileNet-v1 + 4bit-1dim model compression using DKM	Top-1	69.63	—	Unverified
5	ResNet-18 + 2bit-1dim model compression using DKM	Top-1	68.63	—	Unverified
6	MobileNet-v1 + 2bit-1dim model compression using DKM	Top-1	67.62	—	Unverified
7	ResNet-18 + 4bit-4dim model compression using DKM	Top-1	66.1	—	Unverified
8	ResNet-18 + 2bit-2dim model compression using DKM	Top-1	64.7	—	Unverified
9	MobileNet-v1 + 4bit-4dim model compression using DKM	Top-1	61.4	—	Unverified
10	ResNet-18 + 1bit-1dim model compression using DKM	Top-1	59.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MobileBERT + 2bit-1dim model compression using DKM	Accuracy	82.13	—	Unverified
2	MobileBERT + 1bit-1dim model compression using DKM	Accuracy	63.17	—	Unverified